CMSC 471
Spring 2014
Class #1
Tue 1/28/14
Course Overview / Lisp Introduction
Professor Marie desJardins, mariedj@cs.umbc.edu,
ITE 337, x53967
TA: TBA
Today’s Class
• Course overview
• Introduction
– Brief history of AI
– What is AI? (and why is it so cool?)
– What’s the state of AI now?
• Lisp – a first look
Course Overview
In-Class Device Policy
• General policy: no devices during class!!
• That means laptops, tablets, netbooks, phones, ...
• Research shows that information is retained better when recorded through writing
than when recorded through typing
• Research also shows that students using devices are more distracted in class, as
are their neighbors
• Slides will always be posted to the website after class
• Two exceptions:
• At specified times, we’ll have a “laptop lab” to work on Lisp programming
• At other times, we may use device-based Piazza quizzes to check understanding
• I’ll let you know in advance so you can have the appropriate device with you
• If you have a significant reason that you must use a device to
take notes, please speak to me after class today
Course Materials
• Course website:
http://www.cs.umbc.edu/courses/undergraduate/471/spring14/
– Course description and policies (main page)
– Course syllabus, schedule (subject to change!), and slides
– Pointers to homeworks and papers (send me URLs for interesting / relevant
websites, and I’ll add them to the page!)
• Course discussion board: Piazza
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Accept the invitation you should have received by email
If you did not receive an invitation, email me to request to be added!!
Post (and feel free to answer(*)) general questions to Piazza
Requests for extensions, inquiries about status, requests for appointments
should go directly to Prof. desJardins and/or the TA
(*) modulo course academic integrity policy...
Coursework
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Homeworks (36%)
Final project (25%)
Midterm (14%)
Final exam (20%)
Class participation (5%)
Homework and Grading Policies
• Six homework assignments (mix of written and programming)
• Homeworks may be completed in groups of 2 or 3 (see syllabus and grading
policy for full details)
• Single submission for written parts
• Individual submissions for programming parts, but you may help each other (not write each
other’s code!)
• Due every other week (approximately) at the beginning of class
• Late policy applies! (25% penalty per day or fraction thereof, starting after
10-minute grace period)
• Requests for extensions with reasonable cause will be accepted if made well
in advance
• Last-minute requests for extensions will be denied other than in extraordinary
circumstances (documented illness, death in the family, etc.)
• “I have other projects due” is not usually an extraordinary circumstance (I give you a lot of
lead time!)
• Nor is “I didn’t start early enough”
• All inquiries about homework grading (including requests for regrading or
grade adjustments) should be brought to the TA first
Academic Integrity
• Instructor’s responsibilities:
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Be respectful
Be fair
Be available
Tell the students what they need to know and how they will be graded
• Students’ responsibilities:
– Be respectful
– Do not cheat, plagiarize, or lie, or help anyone else to do so
– Do not interfere with other students’ academic activities
Academic Integrity Policy
“By enrolling in this course, each student assumes the
responsibilities of an active participant in UMBC’s
scholarly community, in which everyone’s academic work
and behavior are held to the highest standards of honesty.
Cheating, fabrication, plagiarism, and helping others to
commit these acts are all forms of academic dishonesty,
and they are wrong. Academic misconduct could result in
disciplinary action that may include, but is not limited to,
suspension or dismissal.”
[Statement adopted by UMBC’s Undergraduate Council
and Provost’s Office]
Plagiarism
• REPRESENTING SOMEBODY ELSE’S
WORDS AS YOUR OWN IS PLAGIARISM.
• “But I listed the reference in the bibliography.”
• If you didn’t explicitly quote the text you used, and cite the
source where you used the text, it is plagiarism.
• “But I only used some of the words.”
• Scattering some of your own words and rephrasing isn’t
enough; if the ideas are not restated entirely in your own
words, it is plagiarism.
Plagiarism
• “But only the introduction and background material
are borrowed; all of the original research is mine.”
• If somebody else’s words appear in any document that you
have represented to be written by you, it is plagiarism.
• “But it was only a draft / not an official classroom
assignment, so I didn’t think it counted.”
• If you represented somebody else’s words as your own, even
in an informal context, it is plagiarism.
Plagiarism
• “But the professor told me to use that source!”
• Unless you are explicitly told to copy a quote from a
source, you must write your answers in your own words
even if you use a specified source. If somebody else’s
words appear in your assignment without correct
attribution (quotation marks and citation at the point of
the quote), it is plagiarism.
• Sometimes attribution gets overlooked through oversight,
but it is your responsibility to minimize the possibility that
this can happen.
Plagiarism Exercise
Original passage:
I pledge allegiance to the flag of the United States of
America, and to the republic for which it stands, one
nation, indivisible, with liberty and justice for all.
Unacceptable summary:
I promise loyalty to the United States flag, and to the
country for which it stands, one nation, with freedom
and fairness for all.
Plagiarism Exercise II
Original passage:
I pledge allegiance to the flag of the United States of
America, and to the republic for which it stands, one
nation, indivisible, with liberty and justice for all.
Acceptable summary:
The Pledge of Allegiance represents a promise to be loyal
to the United States of America, and restates the
premises of American government: independent states
united by the ideals of freedom and democracy.
Abetting
• Helping another student to cheat, falsify, or
plagiarize will generally result in your receiving
the same penalty
• Know what your project partners are doing; if you
turn a blind eye to their cheating, you may be
hurting yourself
Penalties
• I take cheating and plagiarism very seriously
• Typical penalties depend on the severity, and whether it is
a first offense. The minimum penalties are:
• Receiving a zero on an assignment (even if only part of the
assignment was plagiarized or copied)
• Being required to redo the assignment, without credit, in order to
pass the class
• Additional penalties may include:
• Receiving a full grade reduction in the class (e.g., an A becomes a
B, a B becomes a C)
• Failing the class (without possibility of dropping it)
• Suspension or expulsion from the university
About Groupwork
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Study groups are encouraged! The material is much easier to learn if you are
discussing it with other students
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Talking about the homework is completely acceptable
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Written homework may be submitted by a group (2-3 students) working
together
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Group assignments must be the work of that group’s members alone!
Programming assignments must be submitted individually but you may help
the other members of your group
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Programming assignments must be written by you but your group members may help you
debug / understand what’s wrong with your code
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Copying another group member’s code is NOT acceptable!
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The class project is a group project (1-3 students; details to follow)
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Having somebody else write or debug code for you is unacceptable
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When you go to write your answers or your code, you should be by yourself,
and you should be recording your own understanding of the solution, not
regenerating something that is not your own personal work.
Course Staff Availability
• Prof. Marie desJardins
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mariedj@cs.umbc.edu
Official office hours: Tue. 10:00-11:00 am, Wed. 2:00-3:00 pm (ITE 337)
Appointments may also be made by request (at least 24 hours notice is best)
Drop in whenever my door is open (see posted “semi-open door policy”)
Will try to respond to e-mail and Piazza posts within 24 hours on weekdays
Post general questions (i.e., those that other students may also be wondering
about) to Piazza
• TA Lianjie Sun
• sun11@umbc.edu
• Office hours: Mon. and Thu. 2:00-3:00 pm (ITE 353)
What is AI??
AI: A Vision
Could an intelligent agent living on your home computer
manage your email, coordinate your work and social
activities, help plan your vacations…… even watch
your house while you take those well planned vacations?
Main Goals of AI
Represent and store knowledge
Retrieve and reason about knowledge
Behave intelligently in complex environments
Develop interesting and useful applications
Interact with people, agents, and the environment
Why AI?
• Engineering: To get machines to do a wider variety of useful
things
– e.g., understand spoken natural language, recognize individual people in
visual scenes, find the best travel plan for your vacation, etc.
• Cognitive Science: As a way to understand how natural minds
and mental phenomena work
– e.g., visual perception, memory, learning, language, etc.
• Philosophy: As a way to explore some basic and interesting
(and important) philosophical questions
– e.g., the mind body problem, what is consciousness, etc.
Foundations of AI
Mathematics
Economics
Psychology
Computer
Science &
Engineering
AI
Cognitive
Science
Philosophy
Biology
Linguistics
History
• 1997: Deep Blue beats Garry Kasparov (world champion)
• 1998: Founding of Google
• 2000: Interactive robot pets
• 2004: First DARPA Grand Challenge robot race
• 2004: Commercial recommender systems (TIVO, amazon.com)
• 2007: Checkers is solved!
• 2011: An AI named Watson beats the top Jeopardy! champions
• 2010: Google self-driving cars reach their 1000th mile
Eliza
• ELIZA: A program that simulated a psychotherapist interacting with a
patient and successfully passed the Turing Test.
• Coded at MIT during 1964-1966 by Joel Weizenbaum.
• First script was DOCTOR.
– The script was a simple collection of syntactic patterns not unlike regular
expressions
– Each pattern had an associated reply which might include bits of the input
(after simple transformations (my  your)
• Weizenbaum was shocked at reactions:
– Psychiatrists thought it had potential.
– People unequivocally anthropomorphized.
– Many thought it solved the NL problem.
Eliza
• This is a
transcript of a
conversation
with an Elizalike program
found in
Emacs
• Try typing
Meta-x doctor
in your favorite
Emacs.
• I am the psychotherapist. Please, describe your problems. Each
time you are finished talking, type RET twice.
• i'm having a hard time with my students
• Why do you say that?
• well, they keep asking me for money
• Maybe your plans have something to do with this.
• what plans?
• Is it because of your life that you say what plans?
• No, it's because I didn't know what you were talking about
• Is it because you didn't know what I was talking about that you
came to me?
• no, it's because i wanted to see if you could pass the turing test
• Does it bother you that you wanted to see if I could really pass
the turing test?
• …..
• How do you reconcile problems at school?
• This is a waste of time. Good bye
• My secretary will send you a bill.
Colby’s PARRY
• Kenneth Colby modeled a
paranoid using the same
techniques circa 1968.
• PARRY has basic emotions.
If it gets angry, its replies
become more hostile.
• In a Turing test, expert
psychiatrists were unable to
distinguish PARRY’s
ramblings from those of real
paranoids.
H: Good afternoon. My name is
Doctor Jones.
P: Pleased to meet you.
H: How are you?
P: All right I guess.
H: Could you tell me your name?
P: Pat Smith.
H: I though you might have some
problems.
P: I've been upset lately.
H: By what?
P: People get on my nerves
sometimes.
H: Tell me more.
P: Do you know anything about
bookies?
...
Parry
Meets
Eliza
P: I don't understand your motives.
E: Why don't you understand my motives?
P: Shouldn't I be?
E: Why do you say that just now?
P: That's just the way it is.
E: Suppose that's the way it were not.
P: So?
E: I am not sure I understand you fully.
P: You harp on the same questions too much.
E: What does that similarity suggest to you?
P: I don't get you.
E: Do you wish that you did get I?
We’ve Come a Long Way
(or have we?...)
Big Questions
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Can machines think?
If so, how?
If not, why not?
What does this say about human beings?
What does this say about the mind?
What’s Easy and What’s Hard?
• It’s been easier to mechanize many of the high-level tasks we usually
associate with “intelligence” in people
– e.g., symbolic integration, proving theorems, playing chess, medical
diagnosis
• It’s been very hard to mechanize tasks that lots of animals can do
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walking around without running into things
catching prey and avoiding predators
interpreting complex sensory information (e.g., visual, aural, …)
modeling the internal states of other animals from their behavior
working as a team (e.g., with pack animals)
• Is there a fundamental difference between the two categories?
Turing Test
• Three rooms contain a person, a computer, and an interrogator.
• The interrogator can communicate with the other two by teleprinter.
• The interrogator tries to determine which is the person and which is the
machine.
• The machine tries to fool the interrogator into believing that it is the
person.
• If the machine succeeds, then we conclude that the machine can think.
The Loebner Contest
• A modern version of the Turing Test, held annually, with a $100,000 cash
prize.
• Hugh Loebner was once director of UMBC’s Academic Computing
Services (née UCS)
• http://www.loebner.net/Prizef/loebner-prize.html
• Restricted topic (removed in 1995) and limited time.
• Participants include a set of humans and a set of computers and a set of
judges.
• Scoring
– Rank from least human to most human.
– Highest median rank wins $2000.
– If better than a human, win $100,000. (Nobody yet…)
What Can AI Systems Do?
Here are some example applications
• Computer vision: face recognition from a large set
• Robotics: autonomous (mostly) automobile
• Natural language processing: simple machine translation
• Expert systems: medical diagnosis in a narrow domain
• Spoken language systems: ~1000 word continuous speech
• Planning and scheduling: Hubble Telescope experiments
• Learning: text categorization into ~1000 topics
• User modeling: Bayesian reasoning in Windows help (the infamous
paper clip…)
• Games: Grand Master level in chess (world champion), perfect play in
checkers, professional-level Go players
What Can’t AI Systems Do Yet?
• Understand natural language robustly (e.g., read and understand articles
in a newspaper)
• Surf the web
• Interpret an arbitrary visual scene
• Learn a natural language
• Play Go as well as the best human players
• Construct plans in dynamic real-time domains
• Refocus attention in complex environments
• Perform life-long learning
Who Does AI?
• Academic researchers (perhaps the most Ph.D.-generating area of
computer science in recent years)
– Some of the top AI schools: CMU, Stanford, Berkeley, MIT, UIUC, UMd,
U Alberta, UT Austin, ... (and, of course, UMBC!)
• Government and private research labs
– NASA, NRL, NIST, IBM, AT&T, SRI, ISI, MERL, ...
• Lots of companies!
– Google, Microsoft, Honeywell, Teknowledge, SAIC, MITRE, Fujitsu,
Global InfoTek, BodyMedia, ...
Applications
Game Playing
45
Text/Sketch Recognition
46
User Modeling/Recommender
Systems
47
Robotics
48
Knowledge Representation
Watson
49
49
Evolutionary Art
50
Computer Vision
51
Possible Approaches
Like
humans
Think
GPS
Act
Eliza
Well
Rational
agents
Heuristic
systems
AI tends to
work mostly
in this area
Like
humans
Think Well
Think
Act
Well
GPS
Rational
agents
Eliza
Heuristic
systems
• Develop formal models of knowledge representation,
reasoning, learning, memory, and problem solving, that can
be rendered in algorithms.
• There is often an emphasis on systems that are provably
correct, and guarantee finding an optimal solution.
Like
humans
Act Well
Think
Act
Well
GPS
Rational
agents
Eliza
Heuristic
systems
• For a given set of inputs, generate an appropriate output that is not
necessarily correct but gets the job done.
• A heuristic (heuristic rule, heuristic method) is a rule of thumb,
strategy, trick, simplification, or any other kind of device which
drastically limits search for solutions in large problem spaces.
• Heuristics do not guarantee optimal solutions; in fact, they do not
guarantee any solution at all: all that can be said for a useful heuristic
is that it offers solutions which are good enough most of the time.
– Feigenbaum and Feldman, 1963, p. 6
Like
humans
Think Like Humans
Think
GPS
Well
Rational
agents
Heuristic
Eliza
• Cognitive science approach
Act
systems
• Focus not just on behavior and I/O
but also look
at reasoning process.
• Computational model should reflect “how” results were obtained.
• Provide a new language for expressing cognitive theories and new
mechanisms for evaluating them
• GPS (General Problem Solver): Goal not just to produce humanlike
behavior (like ELIZA), but to produce a sequence of steps of the
reasoning process that was similar to the steps followed by a person in
solving the same task.
Like
humans
Act Like Humans
• Behaviorist approach.
• Not interested in how you get results, just the
similarity to what human results are.
• Exemplified by the Turing Test (Alan Turing,
1950).
Think
Act
Well
GPS
Rational
agents
Eliza
Heuristic
systems
Homework
• Survey (due this Thursday, 1/30, or just give to me after
class today)
• Pretest (due next Tuesday, 2/4)
• Download clisp
•
[You can use your laptop in class during Thursday’s “laptop lab”
if you have clisp installed or ssh to the gl machines...]
• Write some Lisp programs
• Get started on HW 1 (due Thursday 2/13)
• NOTE: If you’re on the waiting list, come talk to me after
class!!!